class_docstr: The ``PointCloudObjectDetector`` is a :class:`~flash.core.classification.ClassificationTask` that classifies
  pointcloud data.
  
  Args:
      num_classes: The number of classes (outputs) for this :class:`~flash.core.model.Task`.
      backbone: The backbone name (or a tuple of ``nn.Module``, output size) to use.
      backbone_kwargs: Any additional kwargs to pass to the backbone constructor.
      loss_fn: The loss function to use. If ``None``, a default will be selected by the
          :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      metrics: Any metrics to use with this :class:`~flash.core.model.Task`. If ``None``, a default will be selected
          by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
      learning_rate: The learning rate for the optimizer.
      lambda_loss_cls: The value to scale the loss classification.
      lambda_loss_bbox: The value to scale the bounding boxes loss.
      lambda_loss_dir: The value to scale the bounding boxes direction loss.
code: def _patch_dataset(self, dataset: InputBase):
      dataset.input_transform_fn = self.model.preprocess
      dataset.transform_fn = self.model.transform
  
imports: import sys
  from typing import Any, Dict, Optional, Tuple, Union
  from torch import Tensor, nn
  from torch.utils.data import DataLoader, Sampler
  import flash
  from flash.core.data.io.input import DataKeys, InputBase
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.data.utilities.collate import wrap_collate
  from flash.core.model import Task
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.apply_func import get_callable_dict
  from flash.core.utilities.stability import beta
  from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
  from flash.pointcloud.detection.backbones import POINTCLOUD_OBJECT_DETECTION_BACKBONES
label_desc: Use image node_id 9 for calling the PointCloudObjectDetector obj's underlying member method code with example usage: obj._patch_dataset(dataset) without return types